论文标题
基于机器学习的非中心晶体材料的预测
Machine Learning based prediction of noncentrosymmetric crystal materials
论文作者
论文摘要
非中心对称材料在许多重要的应用中起着至关重要的作用，例如激光技术，通信系统，量子计算，网络安全等。但是，新型非中心对称材料的实验发现极为困难。在这里，我们提出了一个机器学习模型，该模型可以预测潜在晶体结构的组成是否为中心对称。通过评估使用Matminer Fearturizer软件包计算出的一套多样化的组合功能，再加上不同的机器学习算法，我们发现随机森林分类器为非中性材料材料预测提供了最佳性能，并在与82,506型材料材料项目的数据集中评估10倍的交叉验证时，可与10倍的交叉验证评估时，精度为84.8％。一个只有3个元素的材料训练的随机森林模型可使精度更高86.9％。我们将ML模型应用于我们的逆设计引擎产生的2,000,000个假设材料的潜在非中心材料，并报告了具有2至4个元素的前20名候选非中心材料，并报告了前20个井口候选者
Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems,quantum computing, cybersecurity, and etc. However, the experimental discovery of new noncentrosymmetric materials is extremely difficult. Here we present a machine learning model that could predict whether the composition of a potential crystalline structure would be centrosymmetric or not. By evaluating a diverse set of composition features calculated using matminer featurizer package coupled with different machine learning algorithms, we find that Random Forest Classifiers give the best performance for noncentrosymmetric material prediction, reaching an accuracy of 84.8% when evaluated with 10 fold cross-validation on the dataset with 82,506 samples extracted from Materials Project. A random forest model trained with materials with only 3 elements gives even higher accuracy of 86.9%. We apply our ML model to screen potential noncentrosymmetric materials from 2,000,000 hypothetical materials generated by our inverse design engine and report the top 20 candidate noncentrosymmetric materials with 2 to 4 elements and top 20 borate candidates
